CorentinJ/TorchStream

LLMs-from-scratch is Sebastian Raschka's GitHub companion to the Manning book Build a Large Language Model (From Scratch), ISBN 9781633437166. It walks through building, pretraining, and finetuning a GPT-style model in PyTorch across seven chapters and five appendices, plus a growing set of bonus notebooks covering newer open architectures.

TensorFlow is Google's open-source, end-to-end platform for machine learning, hosted at tensorflow/tensorflow under the Apache-2.0 license. It was originally built within Google Brain's Machine Intelligence team for ML and neural network research, and today it ships stable Python and C++ APIs alongside GPU, CPU-only, and Docker install paths. The README positions it as covering both research work and shipping ML-powered applications.

AutoGPT is an open-source platform designed for building, deploying, and running AI agents that can carry out complete workflows. Users can define tasks in plain English or use a visual builder to shape each step. The project offers two primary paths: a managed, hosted AutoGPT Platform that handles infrastructure and model access for a fee, and a self-hosting option that is free but requires users to provide their own infrastructure and model API keys. Agents can run on demand, on schedules, or from triggers, connecting to over 45 platforms and hundreds of AI models. It's presented as a tool to automate various functions, from executive operations and sales research to marketing campaign drafts and incident triage in engineering.

Hugging Face Transformers (huggingface/transformers) is a Python library that centralizes model definitions for text, computer vision, audio, video, and multimodal machine learning, covering both inference and training. The README describes it as a pivot point compatible with training frameworks such as Axolotl, DeepSpeed, and PyTorch-Lightning, and inference engines such as vLLM, SGLang, and TGI, with more than 1M+ model checkpoints listed on the Hugging Face Hub.